Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Translating network models to parallel hardware in NEURON.

M L Hines1, N T Carnevale

  • 1Department of Computer Science, Yale University, New Haven, CT, USA. michael.hines@yale.edu

Journal of Neuroscience Methods
|November 13, 2007
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Calcium regulation of HCN channels supports persistent activity in a multiscale model of neocortex.

Neuroscience·2016
Same author

Comparisons between Active Properties of Distal Dendritic Branches and Spines: Implications for Neuronal Computations.

Journal of cognitive neuroscience·2013
Same author

Model structure analysis in NEURON : toward interoperability among neural simulators.

Methods in molecular biology (Clifton, N.J.)·2008
Same author

Parallel network simulations with NEURON.

Journal of computational neuroscience·2006
Same author

The role of distal dendritic gap junctions in synchronization of mitral cell axonal output.

Journal of computational neuroscience·2005
Same author

NEURON: a tool for neuroscientists.

The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry·2001
Same journal

Detection of cochlear microphonic for differential diagnosis between auditory neuropathy mice and noise-induced sensorineural hearing loss mice.

Journal of neuroscience methods·2026
Same journal

Assessment metrics for pain control in rats: A methodological commentary.

Journal of neuroscience methods·2026
Same journal

Infant EEG preprocessing pipelines: A capability framework and current gaps in practice.

Journal of neuroscience methods·2026
Same journal

Methods for measuring neural activity during voluntary wheel running.

Journal of neuroscience methods·2026
Same journal

Serotype-dependent differences in AAV cellular transduction rates in the hypothalamus of Arctic ground squirrels.

Journal of neuroscience methods·2026
Same journal

Rapid generation of human sensory neurons from iPSC for modeling of peripheral neuropathies.

Journal of neuroscience methods·2026
See all related articles

Computational neuroscientists can now run complex network models on parallel hardware without modification. This approach ensures identical numerical results on both serial and parallel systems, simplifying model development and execution.

Area of Science:

  • Computational Neuroscience
  • High-Performance Computing
  • Scientific Modeling

Background:

  • Increasing complexity of neural network models leads to significant computational demands.
  • Parallel computing hardware (clusters, supercomputers) is becoming more accessible to researchers.
  • A practical challenge exists in migrating existing single-processor models to parallel architectures.

Purpose of the Study:

  • To demonstrate a method for transitioning NEURON-implemented network models from serial to parallel hardware.
  • To ensure that models run on parallel hardware produce numerically identical results compared to serial execution.
  • To enable seamless model development on local resources and execution on large-scale parallel systems.

Main Methods:

  • Implementation of a transition strategy for NEURON models.

Related Experiment Videos

  • Testing model execution on both serial and parallel computing environments.
  • Verification of numerical result consistency across different hardware architectures.

    • Main Results:

    • A method was successfully developed to adapt NEURON models for parallel execution.
    • The adapted models produced numerically identical outputs on both serial and parallel hardware.
    • The transition process allows for unmodified code execution on diverse computing platforms.

    Conclusions:

    • It is feasible to migrate NEURON models to parallel hardware while maintaining numerical accuracy.
    • This approach facilitates efficient development and large-scale simulation of complex neural networks.
    • Researchers can leverage accessible local resources for development and powerful supercomputers for execution without code changes.